OpenRaiser/Intern-Atlas huggingface dataset:
A Receding Horizon Scheduling Approach for Search & Rescue Scenarios
Venue: arXiv cs.MA 2020
Year: 2020

Many applications involving complex multi-task problems such as disaster relief, logistics and manufacturing necessitate the deployment and coordination of heterogeneous multi-agent systems due to the sheer number of tasks that must be executed simultaneously. A fundamental requirement for the successful coordination of such systems is leveraging the specialization of each agent within the team. This work presents a Receding Horizon Planning (RHP) framework aimed at scheduling tasks for heterogeneous multi-agent teams in a robust manner. In order to allow for the modular addition and removal of different types of agents to the team, the proposed framework accounts for the capabilities that each agent exhibits (e.g. quadrotors are agile and agnostic to rough terrain but are not suited to transport heavy payloads). An instantiation of the proposed RHP is developed and tested for a search and rescue scenario. Moreover, we present an abstracted search and rescue simulation environment, where a heterogeneous team of agents is deployed to simultaneously explore the environment, find and rescue trapped victims, and extinguish spreading fires as quickly as possible. We validate the effectiveness of our approach through extensive simulations comparing the presented framework with various planning horizons to a greedy task allocation scheme.
An Experimental Study on Microservices based Edge Computing Platforms
Venue: arXiv cs.DC 2020
Year: 2020

The rapid technological advances in the Internet of Things (IoT) allows the blueprint of Smart Cities to become feasible by integrating heterogeneous cloud/fog/edge computing paradigms to collaboratively provide variant smart services in our cities and communities. Thanks to attractive features like fine granularity and loose coupling, the microservices architecture has been proposed to provide scalable and extensible services in large scale distributed IoT systems. Recent studies have evaluated and analyzed the performance interference between microservices based on scenarios on the cloud computing environment. However, they are not holistic for IoT applications given the restriction of the edge device like computation consumption and network capacity. This paper investigates multiple microservice deployment policies on the edge computing platform. The microservices are developed as docker containers, and comprehensive experimental results demonstrate the performance and interference of microservices running on benchmark scenarios.
Optimal Virtual Cluster-based Multiprocessor Scheduling
Venue: arXiv cs.OS 2020
Year: 2020

Scheduling of constrained deadline sporadic task systems on multiprocessor platforms is an area which has received much attention in the recent past. It is widely believed that finding an optimal scheduler is hard, and therefore most studies have focused on developing algorithms with good processor utilization bounds. These algorithms can be broadly classified into two categories: partitioned scheduling in which tasks are statically assigned to individual processors, and global scheduling in which each task is allowed to execute on any processor in the platform. In this paper we consider a third, more general, approach called cluster-based scheduling. In this approach each task is statically assigned to a processor cluster, tasks in each cluster are globally scheduled among themselves, and clusters in turn are scheduled on the multiprocessor platform. We develop techniques to support such cluster-based scheduling algorithms, and also consider properties that minimize total processor utilization of individual clusters. In the last part of this paper, we develop new virtual cluster-based scheduling algorithms. For implicit deadline sporadic task systems, we develop an optimal scheduling algorithm that is neither Pfair nor ERfair. We also show that the processor utilization bound of US-EDF{m/(2m-1)} can be improved by using virtual clustering. Since neither partitioned nor global strategies dominate over the other, cluster-based scheduling is a natural direction for research towards achieving improved processor utilization bounds.
Optimal Correlators for Detection and Estimation in Optical Receivers
Venue: arXiv cs.IT 2020
Year: 2020

Motivated by modern applications of light detection and ranging (LIDAR), we study the model of an optical receiver based on an avalanche photo-diode (APD), followed by electronic circuitry for detection of reflected optical signals and estimation of their delay.This model is known to be quite complicated as it consists of at least three different types of noise: thermal noise, shot noise, and multiplicative noise (excess noise) that stems from the random gain associated with the photo-multiplication of the APD. Consequently, the derivation of the optimal likelihood ratio test (LRT) associated with signal detection is a non-trivial task, which has no apparent exact closed--form solution. We consider instead a class of relatively simple detectors, that are based on correlating the noisy received signal with a given deterministic waveform, and our purpose is to characterize the optimal waveform in the sense of the best trade--off between the false-alarm (FA) error exponent and the missed-detection (MD) error exponent. In the same spirit, we also study the problem of estimating the delay on the basis of maximizing the correlation between the received signal and a time-shifted waveform, as a function of this time shift. We characterize the optimal correlator waveform that minimizes the mean square error (MSE) in the regime of high signal-to-noise ratio (SNR). The optimal correlator waveforms for detection and for estimation turn out to be different, but their limiting behavior is the same: when the thermal Gaussian noise is dominant, the optimal correlator waveform becomes proportional to the clean signal, but when the thermal noise is negligible compared to the other noises, then it becomes logarithmic function of the clean signal, as expected.
Variable Shift SDD: A More Succinct Sentential Decision Diagram
Venue: arXiv cs.DS 2020
Year: 2020

The Sentential Decision Diagram (SDD) is a tractable representation of Boolean functions that subsumes the famous Ordered Binary Decision Diagram (OBDD) as a strict subset. SDDs are attracting much attention because they are more succinct than OBDDs, as well as having canonical forms and supporting many useful queries and transformations such as model counting and Apply operation. In this paper, we propose a more succinct variant of SDD named Variable Shift SDD (VS-SDD). The key idea is to create a unique representation for Boolean functions that are equivalent under a specific variable substitution. We show that VS-SDDs are never larger than SDDs and there are cases in which the size of a VS-SDD is exponentially smaller than that of an SDD. Moreover, despite such succinctness, we show that numerous basic operations that are supported in polytime with SDD are also supported in polytime with VS-SDD. Experiments confirm that VS-SDDs are significantly more succinct than SDDs when applied to classical planning instances, where inherent symmetry exists.
Security Analysis and Design for TAGA: a Touch and Go Assistant in the Aerospace Domain
Venue: arXiv cs.CR 2020
Year: 2020

There is currently a drive in the aerospace domain to introduce machine to machine communication over wireless networks to improve ground processes at airports such as refuelling and air conditiong. To this end a session key has to be established between the aircraft and the respective ground unit such as a fuel truck or a pre-conditiong unit. This is to be provided by a `touch and go assistant in the aerospace domain' (TAGA), which allows an operator to pair up a ground unit and an aircraft present at a parking slot with the help of a NFC system. In this paper, we present the results of our security analysis and co-development of requirements, security concepts, and modular verification thereof. We show that by, and only by, a combination of advanced security protocols and local process measures we obtain secure and resilient designs for TAGA. In particular, the design of choice is fully resilient against long-term key compromises and parallel escalation of attacks.
Pandemic Populism: Facebook Pages of Alternative News Media and the Corona Crisis -- A Computational Content Analysis
Venue: arXiv cs.SI 2020
Year: 2020

The COVID-19 pandemic has not only had severe political, economic, and societal effects, it has also affected media and communication systems in unprecedented ways. While traditional journalistic media has tried to adapt to the rapidly evolving situation, alternative news media on the Internet have given the events their own ideological spin. Such voices have been criticized for furthering societal confusion and spreading potentially dangerous "fake news" or conspiracy theories via social media and other online channels. The current study analyzes the factual basis of such fears in an initial computational content analysis of alternative news media's output on Facebook during the early Corona crisis, based on a large German data set from January to the second half of March 2020. Using computational content analysis, methods, reach, interactions, actors, and topics of the messages were examined, as well as the use of fabricated news and conspiracy theories. The analysis revealed that the alternative news media stay true to message patterns and ideological foundations identified in prior research. While they do not spread obvious lies, they are predominantly sharing overly critical, even anti-systemic messages, opposing the view of the mainstream news media and the political establishment. With this pandemic populism, they contribute to a contradictory, menacing, and distrusting worldview, as portrayed in detail in this analysis.
A Norm Emergence Framework for Normative MAS -- Position Paper
Venue: arXiv cs.MA 2020
Year: 2020

Norm emergence is typically studied in the context of multiagent systems (MAS) where norms are implicit, and participating agents use simplistic decision-making mechanisms. These implicit norms are usually unconsciously shared and adopted through agent interaction. A norm is deemed to have emerged when a threshold or predetermined percentage of agents follow the "norm". Conversely, in normative MAS, norms are typically explicit and agents deliberately share norms through communication or are informed about norms by an authority, following which an agent decides whether to adopt the norm or not. The decision to adopt a norm by the agent can happen immediately after recognition or when an applicable situation arises. In this paper, we make the case that, similarly, a norm has emerged in a normative MAS when a percentage of agents adopt the norm. Furthermore, we posit that agents themselves can and should be involved in norm synthesis, and hence influence the norms governing the MAS, in line with Ostrom's eight principles. Consequently, we put forward a framework for the emergence of norms within a normative MAS, that allows participating agents to propose/request changes to the normative system, while special-purpose synthesizer agents formulate new norms or revisions in response to these requests. Synthesizers must collectively agree that the new norm or norm revision should proceed, and then finally be approved by an "Oracle". The normative system is then modified to incorporate the norm.
Semantic Image Search for Robotic Applications
Venue: arXiv cs.IR 2020
Year: 2020

Generalization in robotics is one of the most important problems. New generalization approaches use internet databases in order to solve new tasks. Modern search engines can return a large amount of information according to a query within milliseconds. However, not all of the returned information is task relevant, partly due to the problem of polysemes. Here we specifically address the problem of object generalization by using image search. We suggest a bi-modal solution, combining visual and textual information, based on the observation that humans use additional linguistic cues to demarcate intended word meaning. We evaluate the quality of our approach by comparing it to human labelled data and find that, on average, our approach leads to improved results in comparison to Google searches, and that it can treat the problem of polysemes.
Continuous Motion Planning with Temporal Logic Specifications using Deep Neural Networks
Venue: arXiv cs.AI 2020
Year: 2020

In this paper, we propose a model-free reinforcement learning method to synthesize control policies for motion planning problems with continuous states and actions. The robot is modelled as a labeled discrete-time Markov decision process (MDP) with continuous state and action spaces. Linear temporal logics (LTL) are used to specify high-level tasks. We then train deep neural networks to approximate the value function and policy using an actor-critic reinforcement learning method. The LTL specification is converted into an annotated limit-deterministic Büchi automaton (LDBA) for continuously shaping the reward so that dense rewards are available during training. A naïve way of solving a motion planning problem with LTL specifications using reinforcement learning is to sample a trajectory and then assign a high reward for training if the trajectory satisfies the entire LTL formula. However, the sampling complexity needed to find such a trajectory is too high when we have a complex LTL formula for continuous state and action spaces. As a result, it is very unlikely that we get enough reward for training if all sample trajectories start from the initial state in the automata. In this paper, we propose a method that samples not only an initial state from the state space, but also an arbitrary state in the automata at the beginning of each training episode. We test our algorithm in simulation using a car-like robot and find out that our method can learn policies for different working configurations and LTL specifications successfully.
The current state of automated negotiation theory: a literature review
Venue: arXiv cs.AI 2020
Year: 2020

Automated negotiation can be an efficient method for resolving conflict and redistributing resources in a coalition setting. Automated negotiation has already seen increased usage in fields such as e-commerce and power distribution in smart girds, and recent advancements in opponent modelling have proven to deliver better outcomes. However, significant barriers to more widespread adoption remain, such as lack of predictable outcome over time and user trust. Additionally, there have been many recent advancements in the field of reasoning about uncertainty, which could help alleviate both those problems. As there is no recent survey on these two fields, and specifically not on their possible intersection we aim to provide such a survey here.
Weakly displaying trees in temporal tree-child network
Venue: arXiv cs.DM 2020
Year: 2020

Recently there has been considerable interest in the problem of finding a phylogenetic network with a minimum number of reticulation vertices which displays a given set of phylogenetic trees, that is, a network with minimum hybrid number. Even so, for certain evolutionary scenarios insisting that a network displays the set of trees can be an overly restrictive assumption. In this paper, we consider the less restrictive notion of displaying called weakly displaying and, in particular, a special case of this which we call rigidly displaying. We characterize when two trees can be rigidly displayed by a temporal tree-child network in terms of fork-picking sequences, a concept that is closely related to that of cherry-picking sequences. We also show that, in case it exists, the rigid hybrid number for two phylogenetic trees is given by a minimum weight fork-picking sequence for the trees, and that the rigid hybrid number can be quite different from the related beaded- and temporal-hybrid numbers.
On Evaluating the Quality of Rule-Based Classification Systems
Venue: arXiv cs.AI 2020
Year: 2020

Two indicators are classically used to evaluate the quality of rule-based classification systems: predictive accuracy, i.e. the system's ability to successfully reproduce learning data and coverage, i.e. the proportion of possible cases for which the logical rules constituting the system apply. In this work, we claim that these two indicators may be insufficient, and additional measures of quality may need to be developed. We theoretically show that classification systems presenting "good" predictive accuracy and coverage can, nonetheless, be trivially improved and illustrate this proposition with examples.
AI Giving Back to Statistics? Discovery of the Coordinate System of Univariate Distributions by Beta Variational Autoencoder
Venue: arXiv cs.LG 2020
Year: 2020

Distributions are fundamental statistical elements that play essential theoretical and practical roles. The article discusses experiences of training neural networks to classify univariate empirical distributions and to represent them on the two-dimensional latent space forcing disentanglement based on the inputs of cumulative distribution functions (CDF). The latent space representation has been performed using an unsupervised beta variational autoencoder (beta-VAE). It separates distributions of different shapes while overlapping similar ones and empirically realises relationships between distributions that are known theoretically. The synthetic experiment of generated univariate continuous and discrete (Bernoulli) distributions with varying sample sizes and parameters has been performed to support the study. The representation on the latent two-dimensional coordinate system can be seen as an additional metadata of the real-world data that disentangles important distribution characteristics, such as shape of the CDF, classification probabilities of underlying theoretical distributions and their parameters, information entropy, and skewness. Entropy changes, providing an "arrow of time", determine dynamic trajectories along representations of distributions on the latent space. In addition, post beta-VAE unsupervised segmentation of the latent space based on weight-of-evidence (WOE) of posterior versus standard isotopic two-dimensional normal densities has been applied detecting the presence of assignable causes that distinguish exceptional CDF inputs.
COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Images
Venue: arXiv cs.CV 2020
Year: 2020

Novel Coronavirus disease (COVID-19) has abruptly and undoubtedly changed the world as we know it at the end of the 2nd decade of the 21st century. COVID-19 is extremely contagious and quickly spreading globally making its early diagnosis of paramount importance. Early diagnosis of COVID-19 enables health care professionals and government authorities to break the chain of transition and flatten the epidemic curve. The common type of COVID-19 diagnosis test, however, requires specific equipment and has relatively low sensitivity. Computed tomography (CT) scans and X-ray images, on the other hand, reveal specific manifestations associated with this disease. Overlap with other lung infections makes human-centered diagnosis of COVID-19 challenging. Consequently, there has been an urgent surge of interest to develop Deep Neural Network (DNN)-based diagnosis solutions, mainly based on Convolutional Neural Networks (CNNs), to facilitate identification of positive COVID-19 cases. CNNs, however, are prone to lose spatial information between image instances and require large datasets. The paper presents an alternative modeling framework based on Capsule Networks, referred to as the COVID-CAPS, being capable of handling small datasets, which is of significant importance due to sudden and rapid emergence of COVID-19. Our results based on a dataset of X-ray images show that COVID-CAPS has advantage over previous CNN-based models. COVID-CAPS achieved an Accuracy of 95.7%, Sensitivity of 90%, Specificity of 95.8%, and Area Under the Curve (AUC) of 0.97, while having far less number of trainable parameters in comparison to its counterparts. To further improve diagnosis capabilities of the COVID-CAPS, pre-training based on a new dataset constructed from an external dataset of X-ray images. Pre-training with a dataset of similar nature further improved accuracy to 98.3% and specificity to 98.6%.
Joint Routing and Scheduling for Large-Scale Deterministic IP Networks
Venue: arXiv cs.NI 2020
Year: 2020

With the advent of 5G and the evolution of Internet protocols, industrial applications are moving from vertical solutions to general purpose IP-based infrastructures that need to meet deterministic Quality of Service (QoS) requirements. The IETF DetNet working group aims at providing an answer to this need with support for (i) deterministic worst-case latency and jitter, and (ii) zero packet loss for time-sensitive traffic. In this paper we focus on the joint routing and scheduling problem in large scale deterministic networks using Cycle Specified Queuing and Forwarding (CSQF), an extension of Cyclic Queuing and Forwarding (CQF) with multiple transmission queues and support of segment routing. In this context, we present two centralized algorithms to maximize traffic acceptance for network planning and online flow admission. We propose an effective solution based on column generation and dynamic programming. Thanks to the reinforcement of the model with valid inequalities, we improve the upper bound and the solution. We demonstrate on realistic instances that we reach an optimality gap smaller than 10% in a few seconds. Finally, we also derive an ultra-fast adaptive greedy algorithm to solve the problem at the cost of a small extra gap.
Eisen: a python package for solid deep learning
Venue: arXiv cs.CV 2020
Year: 2020

Eisen is an open source python package making the implementation of deep learning methods easy. It is specifically tailored to medical image analysis and computer vision tasks, but its flexibility allows extension to any application. Eisen is based on PyTorch and it follows the same architecture of other packages belonging to the PyTorch ecosystem. This simplifies its use and allows it to be compatible with modules provided by other packages. Eisen implements multiple dataset loading methods, I/O for various data formats, data manipulation and transformation, full implementation of training, validation and test loops, implementation of losses and network architectures, automatic export of training artifacts, summaries and logs, visual experiment building, command line interface and more. Furthermore, it is open to user contributions by the community. Documentation, examples and code can be downloaded from http://eisen.ai.
Deformable 3D Convolution for Video Super-Resolution
Venue: arXiv cs.CV 2020
Year: 2020

The spatio-temporal information among video sequences is significant for video super-resolution (SR). However, the spatio-temporal information cannot be fully used by existing video SR methods since spatial feature extraction and temporal motion compensation are usually performed sequentially. In this paper, we propose a deformable 3D convolution network (D3Dnet) to incorporate spatio-temporal information from both spatial and temporal dimensions for video SR. Specifically, we introduce deformable 3D convolution (D3D) to integrate deformable convolution with 3D convolution, obtaining both superior spatio-temporal modeling capability and motion-aware modeling flexibility. Extensive experiments have demonstrated the effectiveness of D3D in exploiting spatio-temporal information. Comparative results show that our network achieves state-of-the-art SR performance. Code is available at: https://github.com/XinyiYing/D3Dnet.